scholarly journals Hybrid Technique for Arrival Rate Extraction and Size Reduction using Singular Spectrum Analysis and Fourier Series

2018 ◽  
Vol 141 ◽  
pp. 96-103
Author(s):  
Ahmad Hammoudeh ◽  
Mohammad Al Saaideh ◽  
Ghazi Al-Naymat
Keyword(s):  
Fourier Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Arrival Rate ◽  
Size Reduction ◽  
Hybrid Technique ◽  
Singular Spectrum

A benchmark and real-time estimator for the passenger arrival rate to elevator system

2018 ◽  
Vol 40 (2) ◽  
pp. 135-150
Author(s):  
Ahmad Hammoudeh ◽  
Lutfi Al-Sharif ◽  
Mohammad Al-Shabi
Keyword(s):  
Real Time ◽  
Linear Model ◽  
Kalman Filtering ◽  
Spectrum Analysis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Arrival Rate ◽  
Singular Spectrum ◽  
Group Control ◽  
Weighted Moving Average

Arrival rate is the number of passengers arriving for elevator service in a certain period of time. Arrival rate is fundamental in expressing the heaviness of the traffic. Hence, it is vital for determining the required number of elevators and the specifications of each elevator such as the speed, capacity, and sector sizes. The passenger arrival process is a random process that is full of noise, and a processing step is required to extract the arrival rate from recorded arrival times of passengers. This work develops a real-time estimator and a benchmark for estimating the arrival rate. There are three contributions in this work; the first is suggesting a benchmark for estimating arrival rate; singular spectrum analysis extracts the arrival rate from noisy data. Hence, singular spectrum analysis is suggested as a benchmark for evaluating the performance of other algorithms. Even though singular spectrum analysis is powerful in extracting the arrival rate, it is not convenient for updating the arrival rate in real time. The second contribution is developing a real-time estimator for the passenger arrival rate that updates its parameters dynamically; dynamic exponentially weighted moving average was developed to provide instantaneous arrival rate updates. The third contribution is introducing exponentially weighted moving average as a linear model for passenger arrival, which opens the door to a large number of model-based algorithms in control theory; Kalman filtering was developed in this work on the top of the EWMA linear model. The results of applying Kalman filtering and DEWMA to real-life data show them as efficient methods for estimating passenger arrival rate to the elevators in real time. Practical application: The methods presented in this paper would allow an elevator controller designer to detect the intensity of the passenger arrival rate. By doing this, it is possible for the elevator controller to switch between different group control algorithms. For example, it could decide to switch from conventional group control to sectoring control and vice versa.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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